If AI can now complete our assignments, does AI change our learning goals? In this episode of the AI for Educators Design Lab podcast, Jennifer Maddrell, PhD, explores how AI not only makes assignments more vulnerable but also prompts a review of traditional learning goals. AI isn't just changing what students can produce. It's also revealing that some of our legacy learning goals were written for a time when recall and reproduction were the dominant aims and indicators of learning.

Using her own literature review assignment as an example, Jennifer considers what students should learn when AI can quickly tackle many learning tasks. She walks through five design questions to help you audit whether your current learning goals are still relevant, sufficient, and aligned with what learners need in a world shaped by human-AI collaboration, and concludes with a preview of Episode 3 on AI literacy coming in April.

1. Do your learning goals prioritize content coverage or cognitive capability?
2. Does AI support or undermine the learning goal?
3. Do your learning goals reflect what authentic, discipline-specific performance looks like when AI is available?
4. Do your learning goals encourage metacognitive awareness?
5. What is the best way to make learning visible?

Check out our other free resources for educators:

• 🎙️Next Path Design Podcast Library: https://nextpathdesign.com/podcast
• 🔗 Design Brief Library as a podcast supplement: https://nextpathdesign.com/designbreif
• 📬 Next Path Insights Newsletter: https://nextpathdesign.com/newsletter

00:00 Welcome Back

00:25 Why Learning Goals Shift

2:21 Lit Review Wake Up Call

04:56 Five Design Questions

06:03 Coverage vs Capability

07:55 When AI Helps or Hurts

09:46 Authentic Practice Today

11:28 Metacognition with AI

14:14 Evidence Beyond Products

15:51 Wrap Up and Next Steps

17:49 Design Brief and Episode Three

18:54 Final Thoughts and Thanks